1. Field of the Invention
This invention relates generally to manufacturing, integrated circuit devices, and, more particularly, to an automated integrated circuit device manufacturing facility employing a distributed control system.
2. Description of the Related Art
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group (lot) of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, thermal anneal process tools, implantation tools, etc. The technologies underlying semiconductor process tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the process tools that are currently commercially available suffer certain deficiencies. In particular, some of such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a centralized factory-wide control system to automatically control the operation of the various process tools and related support system. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
Target values for the various processes performed are generally based on design values for the devices being fabricated. For example, a particular process layer may have a target thickness. Operating recipes for deposition tools and/or polishing tools may be automatically controlled to reduce variation about the target thickness. In another example, the critical dimensions of a transistor gate electrode may have an associated target value. The operating recipes of photolithography tools and/or etch tools may be automatically controlled to achieve the target critical dimensions.
Typically, a control model is used to generate control actions for changing the operating recipe settings for a tool being controlled based on feedback or feedforward metrology data collected related to the processing by the tool. To function effectively, a control model must be provided with metrology data in a timely manner and at a quantity sufficient to maintain its ability to predict the future operation of the tool it controls.
Referring to FIG. 1, a simplified block diagram of an illustrative prior art manufacturing system 10 is provided. The illustrative manufacturing system 10 is adapted to fabricate integrated circuit devices including, but not limited to, microprocessors, memory devices, digital signal processors, application specific integrated circuits (ASICs), or other integrated circuit devices. Additionally, the centralized controller 90 may be employed to perform a variety of functions, such as, for example, scheduling of work flow within the manufacturing system and the dispatch of materials, e.g., wafers, to various processing entities within the manufacturing facility.
A network 20 interconnects various components of the manufacturing system 10, allowing them to exchange information. The illustrative manufacturing system 10 includes a plurality of tools 30-80. Each of the tools 30-80 may be coupled to a computer (not shown) for interfacing with the network 20. In the depicted embodiment, the tools 30-80 are grouped into sets of like tools, as denoted by lettered suffixes. For example, the set of tools 30A-30C represent tools of a certain type, such as a chemical mechanical planarization tool. A particular wafer or lot of wafers progresses through the tools 30-80 as it is being manufactured, with each tool 30-80 performing a specific function in the process flow. Exemplary processing tools for a semiconductor device fabrication environment include metrology tools, photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal anneal tools, implantation tools, etc. The tools 30-80 are illustrated in a rank and file grouping for illustrative purposes only. In an actual implementation, the tools 30-80 may be arranged in any physical order or grouping. Additionally, the connections between the tools in a particular grouping are meant to represent connections to the network 20, rather than interconnections between the tools 30-80.
A manufacturing execution system (MES) server or centralized controller 90 directs high level operation of the manufacturing system 10. The MES server 90 may monitor the status of the various entities in the manufacturing system 10 (i.e., lots, tools 30-80) and control the flow of articles of manufacture (e.g., lots of semiconductor wafers) through the process flow, i.e., direct which wafer lots are to be processed in which of the tools 30-80. A database server 91 is provided for storing data related to the status of the various tools and articles of manufacture in the process flow. The database server 91 may store information in one or more data stores 92. The data may include pre-process and post-process metrology data, tool states, lot priorities, operating recipes, etc. The centralized controller 90 may also provide operating recipes to one or more of the tools depicted in FIG. 1. Of course, the controller 90 need not perform all of these functions. Moreover, the functions described for the controller 90 may be performed by one or more computers spread throughout the system 10.
The manufacturing system 10 also includes an illustrative fault detection unit 12 executing on an illustrative workstation or controller 93. The fault detection unit 12 is adapted to perform or control various fault detection routines employed in the manufacturing system 10. For example, the fault detection unit 12 may acquire or access a variety of different types of data acquired regarding the performance or operation of one or more of the process tools within the manufacturing system 10. Based upon an analysis of that data, the fault detection unit or controller 12 may take various actions, such as declare a fault condition has occurred, indicate that a potential fault condition or event may occur, etc.
The particular control models used by the process controllers 95 depend on the type of tool 30-80 being controlled. Typically, the controller 95 may be capable of performing run-to-run control of the various tools under its control. The control models may be developed empirically using commonly known linear or non-linear techniques. The control models may be relatively simple equation-based models (e.g., linear, exponential, weighted average, etc.) or a more complex model, such as a neural network model, principal component analysis (PCA) model, partial least squares projection to latent structures (PLS) model. The specific implementation of the control models may vary depending on the modeling techniques selected and the process being controlled. The selection and development of the particular control models would be within the ability of one of ordinary skill in the art, and accordingly, the control models are not described in greater detail herein for clarity and to avoid obscuring the instant invention.
An exemplary information exchange and process control framework suitable for use in the manufacturing system 10 is an Advanced Process Control (APC) framework, such as may be implemented using the Catalyst system formerly offered by KLA-Tencor, Inc. The Catalyst system uses Semiconductor Equipment and Materials International (SEMI) Computer Integrated Manufacturing (CIM) Framework compliant system technologies and is based the Advanced Process Control (APC) Framework. CIM (SEMI E81-0699—Provisional Specification for CIM Framework Domain Architecture) and APC (SEMI E93-0999—Provisional Specification for CIM Framework Advanced Process Control Component) specifications are publicly available from SEMI, which is headquartered in Mountain View, Calif.
In the continuing effort to reduce costs and improve productivity, many of the activities within a semiconductor manufacturing facility have been or will be automated in the future. The ultimate goal will be to achieve a fully automated integrated circuit manufacturing facility wherein there is limited, if any, involvement by human operators. Unfortunately, the centralized control structure depicted in the illustrative manufacturing system 10 shown in FIG. 1 may not be readily adaptable for use in a fully automated integrated manufacturing facility as many of the control functions are performed by the MES server 90. That is, the centralized control system depicted in FIG. 1 may not be the most efficient as it relates to the operation of a fully automated integrated circuit manufacturing facility. Moreover, the prior art control system depicted in FIG. 1 only provides limited control of the processing operations to be performed in one or more of a plurality of tools 30-80 depicted therein. That is, the MES server 90 is adapted to perform at least some functions as it relates to run-to-run control of the various process tools and some fault detection capability. The MES server 90 may also be involved, at least to some degree, in metrology sampling, scheduling and dispatch of wafer lots, scheduling preventative maintenance procedures to be performed on the various process tools, etc. Many additional functions needed to operate the manufacturing facility 10, e.g., physical movement of wafers, control of processing utilities, are controlled by other personnel or computer systems within the fabrication facility 10.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.